General Introduction Page last changed today See chapter 1 of the book . JavaScript is supported by Mozilla (from Netscape 2), Explorer (from version 3), Opera (from version 3), Safari, iCab, and Konqueror. I regularly test my scripts in these browsers. See also the "Browser compatibility" section on the Resources page . In addition, the following browsers support JavaScript, but I don't test my scripts in them: Hotjava 3 WebTV OmniWeb QNX Voyager NetBox Amiga-AWeb Amiga Voyager Sega Dreamcast Ant Fresco (Risc) as well as several mobile phone browsers This list is not complete. This page has been translated into French and Turkish . JavaScript is most commonly used as a client side scripting language. The fact that the script is in the HTML page means that your scripts can be seen and copied by whoever views your page. JavaScript can be used in other contexts than a Web browser. JavaScript is not the same as Java. JavaScript is not a programming language in strict sense.
How Javascript Loading Works - DOMContentLoaded and OnLoad There are actually three different windows of time during a web request in which we can load and execute javascript. These windows are delineated by the DOMContentLoaded event and the OnLoad event. We can load our scripts before DOMContentLoaded, after DOMContentLoad, and after OnLoad. The Events The DOMContentLoaded event is triggered when the page’s Document Object Model (DOM) is ready. The OnLoad event is triggered when the entire page has loaded. Loading Javascript So how do we hook into each of these events; and more importantly, when should we use each? <! If we examine the javascript console we can see the list of events play out like this: external.js Loaded and Executed DOM Loaded (DOMContentLoaded) dom.js Loaded and Executed Page Loaded (OnLoad) ajax.js Loaded and Executed dom_ajax.js Loaded and Executed page_ajax.js Loaded and Executed This console output implicates the three windows of javascript loading: External javascript (external.js) is loaded before DOMContentLoaded.
Das Master-Theorem | Ein ganz normaler Blog Hi Leute, da ich gerade an den Hausaufgaben für „Algorithmen & Datenstrukturen“ sitze und mit dem so genannten „Master-Theorem“ arbeite, aber derzeit noch auf eine Antwort in einem Forum warten muss will ich hier mal kurz erklären was das Master-Theorem ist. Mit Hilfe des Master-Theorems ist es sehr einfach schnell die Komplexität eines Programmes herauszufinden, welches Rekursionen verwendet. Ich musste mir das Skript zwar 2-3 mal durchlesen und habe auch Mr. G. Oogle danach gefragt, aber nach kurzer Zeit hat es geklickt und es ist auch wirklich nicht sehr schwer. Zunächst betrachten wir die allgemeine Formel des Master-Theorems: T(n) = a*T(n/b) + f(n) Die Gleichung sieht relativ kompliziert aus, versteht man jedoch die einzelnen Teile ist es nicht weiter schwer: Ich möchte euch das Ganze anhand eines Beispiels zeigen und zwar betrachten wir dazu die „Binäre Suche“. Jetzt müssen wir uns noch den Rekursionsexponenten p berechnen. Viel Spaß damit Max
C++ Language Tutorial This website uses cookies. By continuing, you give permission to deploy cookies, as detailed in our privacy policy. ok Search: Not logged in C++ Language These tutorials explain the C++ language from its basics up to the newest features introduced by C++11. Introduction Compilers Basics of C++ Program structure Compound data types Classes Other language features C++ Standard Library Input/Output with files Tutorials C++ LanguageAscii CodesBoolean OperationsNumerical Bases C++ Language Introduction:CompilersBasics of C++:Program structure:Compound data types:Classes:Other language features:Standard library:Input/output with files Data Structures: Objects and Arrays On two occasions I have been asked, ‘Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’ [...] Numbers, Booleans, and strings are the bricks that data structures are built from. The programs we have built so far have been seriously hampered by the fact that they were operating only on simple data types. The chapter will work through a more or less realistic programming example, introducing concepts as they apply to the problem at hand. The weresquirrel Every now and then, usually between eight and ten in the evening, Jacques finds himself transforming into a small furry rodent with a bushy tail. On one hand, Jacques is quite glad that he doesn’t have classic lycanthropy. That takes care of the cat and oak problems. Switching to a more scientific approach, Jacques intends to start keeping a daily log of everything he did that day and whether he changed form. The first thing he does is design a data structure to store this information. Data sets
Lecture 20: Recursion Trees and the Master Method Recursion Trees A recursion tree is useful for visualizing what happens when a recurrence is iterated. It diagrams the tree of recursive calls and the amount of work done at each call. For instance, consider the recurrence T(n) = 2T(n/2) + n2. The recursion tree for this recurrence has the following form: In this case, it is straightforward to sum across each row of the tree to obtain the total work done at a given level: This a geometric series, thus in the limit the sum is O(n2). Recursion trees can be useful for gaining intuition about the closed form of a recurrence, but they are not a proof (and in fact it is easy to get the wrong answer with a recursion tree, as is the case with any method that includes ''...'' kinds of reasoning). Let's consider another example, T(n) = T(n/3) + T(2n/3) + n. Expanding out the first few levels, the recurrence tree is: Note that the tree here is not balanced: the longest path is the rightmost one, and its length is log3/2 n. The Master Method
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